Granular Computing

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Vladimir S. Lerner

The Variation Principle In Informational Macrodynamics (VP) introduces the process' integral information measure, which has a distinctive difference from traditional information approaches that use an entropy function. The VP's minimax mathematical mechanisms formalize the regularities of the cooperative informational dynamics, connecting randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and nonsymmetry, stability and instability, regular and chaotic dynamics, thermodynamics and informational dynamics, time-reversible and time-irreversible processes, reveals the information dynamic mechanisms of evolution and development. The mathematical formalisms and methods, implemented in the forms of computer models, algorithms and programs, provide new general tools for Information Systems' Modeling in such areas as biology, intelligent systems, computer and information technology, including communications, data modeling and data management. Variation Principle In Informational Macrodynamics, with its examples and applications, will meet the needs of a professional audience composed of researchers and practitioners in industry, as well as graduate-level students in computer science, mathematics and engineering.

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Alan F. Murray

Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'. Section C presents a number of examples of `Unsupervised Training'. For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications.

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Mehdi Dastani, Jomi F. Hübner, Brian Logan

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Workshop on Programming Multi-Agents Systems held in Valencia, Spain, in June 2012. The 10 revised full papers presented were carefully selected from 14 submissions covering a wide range of topics in multi-agent system programming languages, including language design and efficient implementation, agent communication, and robot programming. I addition to these regular papers, the volume includes six papers from the Multi-Agent programming Contest 2012 (MAPC).

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Max Bramer, Miltos Petridis

<p>The papers in this volume are the refereed papers presented at AI-2012, the Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2012 in both the technical and the application streams.</p><p>They present new and innovative developments and applications, divided into technical stream sections on Data Mining, Data Mining and Machine Learning, Planning and Optimisation, and Knowledge Management and Prediction, followed by application stream sections on Language and Classification, Recommendation, Practical Applications and Systems, and Data Mining and Machine Learning. The volume also includes the text of short papers presented as posters at the conference.</p><p>This is the twenty-ninth volume in the <i>Research and Development </i><i>in Intelligent Systems</i> series, which also incorporates the twentieth volume in the <i>App</i><i>lications and Innovations in Intelligent Systems</i> series. These series are essential reading for those who wish to keep up to date with developments in this important field.</p>